The geometric structure of the seven-degree-of-freedom (7-DOF) industrial robot mostly meets the Pieper criterion, so the closed solution for the 7-DOF has a lot of room for development. A method based on Radial Basis Function(RBF) neural network to indirectly obtain kinematics inverse solution is proposed. The kinematics equation was transformed into the optimal control problem. The method of combining the genetic algorithm with the best compliance criterion was adopted to provide an accurate sample for the RBF neural network algorithm. In order to improve the convergence speed and the convergence accuracy of the neural network algorithm, an indirect method was adopted to introduce the concept of angle of the connecting rod triangle. In order to verify the reliability of the results, an optimization experiment based on the RBF neural network algorithm for indirect inverse was carried out on a 7-DOF redundant manipulator, and the inverse RBF neural network was compared with the traditional RBF neural network to solve the inverse kinematics algorithm. The results show that the algorithm is simple in structure and can significantly improve convergence accuracy and convergence speed.